Bibliographic record
Abstract
Leatt and Porter offer views on the principles of leadership development for the future of healthcare and propose a "new model for developing healthcare leaders em leader that could transform the educational process and improve outcomes" for organizations as a whole. They maintain that the healthcare "educational process needs to be designed to enhance senior healthcare leaders' competency, prepare them for action and ultimately increase leadership and system performance and quality." As a tool to achieve these goals, Leatt and Porter present a model for learning based on 10 principles that, among other things, address what they see as the "need to increase our ability to identify, quantify, develop, measure and evaluate competencies for healthcare leaders." In the viewpoint of this commentator, written from his observations as a CEO, the most critical competencies in a leader, and the least susceptible to measurement, are creativity and vision. In the business world, as in the healthcare sector, these traits are absolutely necessary for leaders to deal effectively with known and unforeseen demands on their organizations, ongoing problems of scarce resources and, particularly in the Canadian healthcare sector, the need for leaders to negotiate with all levels of government to improve the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.068 | 0.055 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".